Adaptive E-learning Environment Based On Learning Styles And Its Impact .
(2021) 18:53 El‑Sabagh Int J Educ Technol High Educ https://doi.org/10.1186/s41239-021-00289-4 RESEARCH ARTICLE Open Access Adaptive e‑learning environment based on learning styles and its impact on development students’ engagement Hassan A. El‑Sabagh1,2* *Correspondence: haelsabagh@uqu.edu.sa; haelsabagh@mans.edu.eg 1 E‑Learning Deanship, Umm Al-Qura University, Mecca, Saudi Arabia Full list of author information is available at the end of the article Abstract Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students’ learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learn‑ ing environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimen‑ tal research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and develop‑ ing more customized and adaptive e-learning environments to reinforce student engagement. Keywords: Adaptive e-Learning, Learning style, Student engagement, E-Learning, Learning impact Introduction In recent years, educational technology has advanced at a rapid rate. Once learning experiences are customized, e-learning content becomes richer and more diverse (ElSabagh & Hamed, 2020; Yang et al., 2013). E-learning produces constructive learning outcomes, as it allows students to actively participate in learning at anytime and anyplace (Chen et al., 2010; Lee et al., 2019). Recently, adaptive e-learning has become an approach that is widely implemented by higher education institutions. The adaptive The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑ rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 e-learning environment (ALE) is an emerging research field that deals with the development approach to fulfill students’ learning styles by adapting the learning environment within the learning management system "LMS" to change the concept of delivering e-content. Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students’ learning styles or preferences. (Normadhi et al., 2019; Oxman & Wong, 2014). By offering customized content, adaptive e-learning environments improve the quality of online learning. The customized environment should be adaptable based on the needs and learning styles of each student in the same course. (Franzoni & Assar, 2009; Kolekar et al., 2017). Adaptive e-learning changes the level of instruction dynamically based on student learning styles and personalizes instruction to enhance or accelerate a student’s success. Directing instruction to each student’s strengths and content needs can minimize course dropout rates, increase student outcomes and the speed at which they are accomplished. The personalized learning approach focuses on providing an effective, customized, and efficient path of learning so that every student can participate in the learning process (Hussein & Al-Chalabi, 2020). Learning styles, on the other hand, represent an important issue in learning in the twenty-first century, with students expected to participate actively in developing self-understanding as well as their environment engagement. (KlasnjaMilicevic et al., 2011; Nuankaew et al., 2019; Truong, 2016). In current conventional e-learning environments, instruction has traditionally followed a “one style fits all” approach, which means that all students are exposed to the same learning procedures. This type of learning does not take into account the different learning styles and preferences of students. Currently, the development of e-learning systems has accommodated and supported personalized learning, in which instruction is fitted to a students’ individual needs and learning styles (Beldagli & Adiguzel, 2010; Benhamdi et al., 2017; Pashler et al., 2008). Some personalized approaches let students choose content that matches their personality (Hussein & Al-Chalabi, 2020). The delivery of course materials is an important issue of personalized learning. Moreover, designing a well-designed, effective, adaptive e-learning system represents a challenge due to complication of adapting to the different needs of learners (Alshammari, 2016). Regardless of using e-learning claims that shifting to adaptive e-learning environments to be able to reinforce students’ engagement. However, a learning environment cannot be considered adaptive if it is not flexible enough to accommodate students’ learning styles. (Ennouamani & Mahani, 2017). On the other hand, while student engagement has become a central issue in learning, it is also an indicator of educational quality and whether active learning occurs in classes. (Lee et al., 2019; Nkomo et al., 2021; Robinson & Hullinger, 2008). Veiga et al. (2014) suggest that there is a need for further research in engagement because assessing students’ engagement is a predictor of learning and academic progress. It is important to clarify the distinction between causal factors such as learning environment and outcome factors such as achievement. Accordingly, student engagement is an important research topic because it affects a student’s final grade, and course dropout rate (Staikopoulos et al., 2015). The Umm Al-Qura University strategic plan through common first-year deanship has focused on best practices that increase students’ higher-order skills. These skills Page 2 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 include communication skills, problem-solving skills, research skills, and creative thinking skills. Although the UQU action plan involves improving these skills through common first-year academic programs, the student’s learning skills need to be encouraged and engaged more (Umm Al-Qura University Agency, 2020). As a result of the author’s experience, The conventional methods of instruction in the "learning skills" course were observed, in which the content is presented to all students in one style that is dependent on understanding the content regardless of the diversity of their learning styles. According to some studies (Alshammari & Qtaish, 2019; Lee & Kim, 2012; Shih et al., 2008; Verdú, et al., 2008; Yalcinalp & Avc, 2019), there is little attention paid to the needs and preferences of individual learners, and as a result, all learners are treated in the same way. More research into the impact of educational technologies on developing skills and performance among different learners is recommended. This “one-style-fitsall” approach implies that all learners are expected to use the same learning style as prescribed by the e-learning environment. Subsequently, a review of the literature revealed that an adaptive e-learning environment can affect learning outcomes to fill the identified gap. In conclusion: Adaptive e-learning environments rely on the learner’s preferences and learning style as a reference that supports to create adaptation. To confirm the above: the author conducted an exploratory study via an open interview that included some questions with a sample of 50 students in the learning skills department of common first-year. Questions asked about the difficulties they face when learning a "learning skills" course, what is the preferred way of course content. Students (88%) agreed that the way students are presented does not differ according to their differences and that they suffer from a lack of personal learning that is compatible with their style of work. Students (82%) agreed that they lack adaptive educational content that helps them to be engaged in the learning process. Accordingly, the author handled the research problem. This research supplements to the existing body of knowledge on the subject. It is considered significant because it improves understanding challenges involved in designing the adaptive environments based on learning styles parameter. Subsequently, this paper is structured as follows: The next section presents the related work cited in the literature, followed by research methodology, then data collection, results, discussion, and finally, some conclusions and future trends are discussed. Theoretical framework This section briefly provides a thorough review of the literature about the adaptive E-learning environments based on learning styles. Adaptive e‑learning environments based on learning styles The adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. The learning management system offers the same tools to all learners, although individual learners need different details based on learning style and preferences. (Beldagli & Adiguzel, 2010; Kolekar et al., 2017). The interactive e-learning environment requisite evaluating the learner’s desired learning style, before the course delivery, such as an online quiz or during the course delivery, such as tracking student reactions (DeCapua & Marshall, 2015). Page 3 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 In e-learning environments, adaptation is constructed on a series of well-designed processes to fit the instructional materials. The adaptive e-learning framework attempt to match instructional content to the learners’ needs and styles. According to Qazdar et al. (2015), adaptive e-learning (AEL) environments rely on constructing a model of each learner’s needs, preferences, and styles. It is well recognized that such adaptive behavior can increase learners’ development and performance, thus enriching learning experience quality. (Shi et al., 2013). The following features of adaptive e-learning environments can be identified through diversity, interactivity, adaptability, feedback, performance, and predictability. Although adaptive framework taxonomy and characteristics related to various elements, adaptive learning includes at least three elements: a model of the structure of the content to be learned with detailed learning outcomes (a content model). The student’s expertise based on success, as well as a method of interpreting student strengths (a learner model), and a method of matching the instructional materials and how it is delivered in a customized way (an instructional model) (Ali et al., 2019). The number of adaptive e-learning studies has increased over the last few years. Adaptive e-learning is likely to increase at an accelerating pace at all levels of instruction (Hussein & Al-Chalabi, 2020; Oxman & Wong, 2014). Many studies assured the power of adaptive e-learning in delivering e-content for learners in a way that fitting their needs, and learning styles, which helps improve the process of students’ acquisition of knowledge, experiences and develop their higher thinking skills (Ali et al., 2019; Behaz & Djoudi, 2012; Chun-Hui et al., 2017; Daines et al., 2016; Dominic et al., 2015; Mahnane et al., 2013; Vassileva, 2012). Student characteristics of learning style are recognized as an important issue and a vital influence in learning and are frequently used as a foundation to generate personalized learning experiences (Alshammari & Qtaish, 2019; El-Sabagh & Hamed, 2020; Hussein & Al-Chalabi, 2020; Klasnja-Milicevic et al., 2011; Normadhi et al., 2019; Ozyurt & Ozyurt, 2015). The learning style is a parameter of designing adaptive e-learning environments. Individuals differ in their learning styles when interacting with the content presented to them, as many studies emphasized the relationship between e-learning and learning styles to be motivated in learning situations, consequently improving the learning outcomes (Ali et al., 2019; Alshammari, 2016; Alzain et al., 2018a, b; Liang, 2012; Mahnane et al., 2013; Nainie et al., 2010; Velázquez & Assar, 2009). The word "learning style" refers to the process by which the learner organizes, processes, represents, and combines this information and stores it in his cognitive source, then retrieves the information and experiences in the style that reflects his technique of communicating them. (Fleming & Baume, 2006; Jaleel & Thomas, 2019; Jonassen & Grabowski, 2012; Klasnja-Milicevic et al., 2011; Nuankaew et al., 2019; Pashler et al., 2008; Willingham et al., 2105; Zhang, 2017). The concept of learning style is founded based on the fact that students vary in their styles of receiving knowledge and thought, to help them recognizing and combining information in their mind, as well as acquire experiences and skills. (Naqeeb, 2011). The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016), and a few findings on the effect of adapting instruction to learning style. There are many models of learning styles (Aldosarim et al., 2018; Alzain et al., 2018a, 2018b; Cletus & Eneluwe, 2020; Franzoni & Assar, 2009; Willingham et al., 2015), including the VARK model, which is one of the most well-known models used to Page 4 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 Page 5 of 24 classify learning styles. The VARK questionnaire offers better thought about information processing preferences (Johnson, 2009). Fleming and Baume (2006) developed the VARK model, which consists of four students’ preferred learning types. The letter "V" represents for visual and means the visual style, while the letter "A" represents for auditory and means the auditory style, and the letter "R/W" represents "write/read", means the reading/writing style, and the letter "K" represents the word "Kinesthetic" and means the practical style. Moreover, VARK distinguishes the visual category further into graphical and textual or visual and read/write learners (Murphy et al., 2004; Leung, et al., 2014; Willingham et al., 2015). The four categories of The VARK Learning Style Inventory are shown in the Fig. 1 below. According to the VARK model, learners are classified into four groups representing basic learning styles based on their responses which have 16 questions, there are four potential responses to each question, where each answer agrees to one of the extremes of the dimension (Hussain, 2017; Silva, 2020; Zhang, 2017) to support instructors who use it to create effective courses for students. Visual learners prefer to take instructional materials and send assignments using tools such as maps, graphs, images, and other symbols, according to Fleming and Baume (2006). Learners who can read–write prefer to use written textual learning materials, they use glossaries, handouts, textbooks, and lecture notes. Aural learners, on the other hand, prefer to learn through spoken materials, dialogue, lectures, and discussions. Direct practice and learning by doing are preferred by kinesthetic learners (Becker et al., 2007; Fleming & Baume, 2006; Willingham et al., 2015). As a result, this research work aims to provide a comprehensive discussion about how these individual parameters can be applied in adaptive e-learning environment practices. Dominic et al., (2015) presented a framework for an adaptive educational system that personalized learning content based on student learning styles (Felder-Silverman learning model) and other factors such as learners’ learning subject competency level. This framework allowed students to follow their adaptive learning content paths based on filling in "ils" questionnaire. Additionally, providing a customized framework that can automatically respond to students’ learning styles and suggest online activities with complete personalization. Similarly, El Bachari et al. (2011) attempted to determine a student’s unique learning style and then adapt instruction to that individual interests. Adaptive e-learning focused on learner experience and learning style has a higher degree Prefer to use images, maps, illustrations, and videos Prefer to use words, lecture notes, textbooks to learn new information. Fig. 1 VARK learning styles Information concerns in best through ears. Prefer to use tactical representations of information, and hands on to learn new information.
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 of perceived usability than a non-adaptive e-learning system, according to Alshammari et al. (2015). This can also improve learners’ satisfaction, engagement, and motivation, thus improving their learning. According to the findings of (Akbulut & Cardak, 2012; Alshammari & Qtaish, 2019; Alzain et al., 2018a, b; Shi et al., 2013; Truong, 2016), adaptation based on a combination of learning style, and information level yields significantly better learning gains. Researchers have recently initiated to focus on how to personalize e-learning experiences using personal characteristics such as the student’s preferred learning style. Personal learning challenges are addressed by adaptive learning programs, which provide learners with courses that are fit to their specific needs, such as their learning styles. Student engagement Previous research has emphasized that student participation is a key factor in overcoming academic problems such as poor academic performance, isolation, and high dropout rates (Fredricks et al., 2004). Student participation is vital to student learning, especially in an online environment where students may feel isolated and disconnected (Dixson, 2015). Student engagement is the degree to which students consciously engage with a course’s materials, other students, and the instructor. Student engagement is significant for keeping students engaged in the course and, as a result, in their learning (Barkley & Major, 2020; Lee et al., 2019; Rogers-Stacy, et al, 2017). Extensive research was conducted to investigate the degree of student engagement in web-based learning systems and traditional education systems. For instance, using a variety of methods and input features to test the relationship between student data and student participation (Hussain et al., 2018). Guo et al. (2014) checked the participation of students when they watched videos. The input characteristics of the study were based on the time they watched it and how often students respond to the assessment. Atherton et al. (2017) found a correlation between the use of course materials and student performance; course content is more expected to lead to better grades. Pardo et al., (2016) found that interactive students with interactive learning activities have a significant impact on student test scores. The course results are positively correlated with student participation according to previous research. For example, Atherton et al. (2017) explained that students accessed learning materials online and passed exams regularly to obtain higher test scores. Other studies have shown that students with higher levels of participation in questionnaires and course performance tend to perform well (Mutahi et al., 2017). Skills, emotion, participation, and performance, according to Dixson (2015), were factors in online learning engagement. Skills are a type of learning that includes things like practicing on a daily foundation, paying attention while listening and reading, and taking notes. Emotion refers to how the learner feels about learning, such as how much you want to learn. Participation refers to how the learner act in a class, such as chat, discussion, or conversation. Performance is a result, such as a good grade or a good test score. In general, engagement indicated that students spend time, energy learning materials, and skills to interact constructively with others in the classroom, and at least participate in emotional learning in one way or another (that is, be motivated by an idea, willing to learn and interact). Student engagement is produced through personal attitudes, Page 6 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 thoughts, behaviors, and communication with others. Thoughts, effort, and feelings to a certain level when studying. Therefore, the student engagement scale attempts to measure what students are doing (thinking actively), how they relate to their learning, and how they relate to content, faculty members, and other learners including the following factors as shown in Fig. 2. (skills, participation/interaction, performance, and emotions). Hence, previous research has moved beyond comparing online and face-to-face classes to investigating ways to improve online learning (Dixson, 2015; Gaytan & McEwen, 2007; Lévy & Wakabayashi, 2008; Mutahi et al., 2017). Learning effort, involvement in activities, interaction, and learning satisfaction, according to reviews of previous research on student engagement, are significant measures of student engagement in learning environments (Dixson, 2015; Evans et al., 2017; Lee et al., 2019; Mutahi et al., 2017; RogersStacy et al., 2017). These results point to several features of e-learning environments that can be used as measures of student participation. Successful and engaged online learners learn actively, have the psychological inspiration to learn, make good use of prior experience, and make successful use of online technology. Furthermore, they have excellent communication abilities and are adept at both cooperative and self-directed learning (Dixson, 2015; Hong, 2009; Nkomo et al., 2021). Overview of designing the adaptive e‑learning environment The paper follows the (ADDIE) Instructional Design Model: analysis, design, develop, implement, and evaluate to answer the first research question. The adaptive learning environment offers an interactive decentralized media environment that takes into account individual differences among students. Moreover, the environment can spread the culture of self-learning, attract students, and increase their engagement in learning. Any learning environment that is intended to accomplish a specific goal should be consistent to increase students’ motivation to learn. so that they have content that is personalized to their specific requirements, rather than one-size-fits-all content. As a result, a set of instructional design standards for designing an adaptive e-learning framework based on learning styles was developed according to the following diagram (Fig. 3). According to the previous figure, The analysis phase included identifying the course materials and learning tools (syllabus and course plan modules) used for the study. The learning objectives were included in the high-level learning objectives (C4-C6: analysis, synthesis, evaluation). Fig. 2 Engagement factors Page 7 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 Fig. 3 The ID (model) of the adaptive e-learning environment Fig. 4 Adaptive e-course design The design phase included writing SMART objectives, the learning materials were written within the modules plan. To support adaptive learning, four content paths were identified, choosing learning models, processes, and evaluation. Course structure and navigation were planned. The adaptive structural design identified the relationships between the different components, such as introduction units, learning materials, quizzes. Determining the four path materials. The course instructional materials were identified according to the following Figure 4. The development phase included: preparing and selecting the media for the e-course according to each content path in an adaptive e-learning environment. During this process, the author accomplished the storyboard and the media to be included on Page 8 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 each page of the storyboard. A category was developed for the instructional media for each path (Fig. 5) The author developed a learning styles questionnaire via a mobile App. as follows: https:// play. google. com/ store/ apps/ detai ls? id com. point abili ty. vark. Then, the students accessed the adaptive e-course modules based on their learning styles. The Implementation phase involved the following: The professional validation of the course instructional materials. Expert validation is used to evaluate the consistency of course materials (syllabi and modules). The validation was performed including the following: student learning activities, learning implementation capability, and student reactions to modules. The learner’s behaviors, errors, navigation, and learning process are continuously geared toward improving the learner’s modules based on the data the learner gathered about him. The Evaluation phase included five e-learning specialists who reviewed the adaptive e-learning. After that, the framework was revised based on expert recommendations and feedback. Content assessment, media evaluation in three forms, instructional design, interface design, and usage design included in the evaluation. Adaptive learners checked the proposed framework. It was divided into two sections. Pilot testing where the proposed environment was tested by ten learners who represented the sample in the first phase. Each learner’s behavior was observed, questions were answered, and learning control, media access, and time spent learning were all verified. Research methodology Research Purpose and Questions This research aims to investigate the impact of designing an adaptive e-learning environment on the development of students’ engagement. The research conceptual framework is illustrated in Fig. 6. Therefore, the articulated research questions are as follows: the main research question is "What is the impact of an adaptive e-learning environment based on (VARK) learning styles on developing students’ engagement? Fig. 5 Roles and deployment diagram of the adaptive e-learning environment Page 9 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 Fig. 6 The conceptual framework (model) of the research questions Accordingly, there are two sub research questions a) "What is the instructional design of the adaptive e-learning environment?" b) "What is the impact of an adaptive e-learning based on (VARK) learning styles on development students’ engagement (skills, participation, performance, emotional) in comparison with conventional e-learning?". Research hypotheses The research aims to verify the validity of the following hypothesis: H1: There is no statistically significant difference between the students’ mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students’ engagement scale. H2: There is a statistically significant difference at the level of (0.05) between the students’ mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students’ engagement factors in favor of the experimental group. Research design This research was a quasi-experimental research with the pretest-posttest. Research variables were independent and dependent as shown in the following Fig. 7. Both groups were informed with the learning activities tracks, the experimental group was instructed to use the adaptive learning environment to accomplish the learning goals; on the other hand, the control group was exposed to the conventional e-learning environment without the adaptive e-learning parameters. Page 10 of 24
El‑Sabagh I nt J Educ Technol High Educ (2021) 18:53 Experimental group Page 11 of 24 Pretest - Students' engagement scale Treatment (Adaptive e-learning environment) Posttest - Students' engagement scale Quasi-Experiment Control group Pretest - Students' engagement scale Posttest - Students' engagement scale Conventional elearning environment C o m p a r i s o Fig. 7 Research "Experimental" design Table 1 Students’ demographic data Age Gender M Total F Experimental students 36 24 60 Control group 31 27 58 Research participants The sample consisted of students studying the "learning skills" course in the common first-year deanship aged b
Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students' learning styles or preferences. (Nor- . e adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. e learning management
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